SKILLEMALL.ai

AB solarpunk

Create, explore, critique, and practice solarpunk across visual art and image prompts, fiction and worldbuilding, fashion, games, education, everyday living, adaptive reuse, ecological design, parklets, guerrilla gardening, urban and rural planning, community infrastructure, and hopeful collective futures. Use for solarpunk aesthetics, stories, values, or practical paths toward equitable ecological flourishing. Do not use for generic sustainability tasks with no solarpunk intent, certified engineering, jurisdiction-specific legal advice, or operating an image or infrastructure tool; pair with the relevant specialist.

The skillemall take

Promises guidance on solarpunk creation across visual art, fiction, fashion, urban planning, and everyday practice. The folder contains 21 files with no critical errors. Quality scores are solid: 89 for content, 100 for safety. Process score of 70 signals a gap between scope and execution depth.

In practice, it functions as a reference with examples and directions rather than a tool with concrete outputs. Scope is clearly bounded: excludes engineering specs, legal advice, and infrastructure operation. Use it if you need an inspiring framework for brainstorming ecological design or worldbuilding. Skip it if you expect step-by-step solutions.

magnus919/agent-skills Agent Skills author: magnus919 21 files body ≈ 2 199 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Create, explore, critique, and practice solarpunk across visual art and image prompts, fiction and worldbuilding, fashion, games, education, everyday living…

As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
70/100
Nearly there
Inputs and preconditions w 11
0
Result and completion w 14
40
When it triggers w 12
50
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 7 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2199 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium 15 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 624: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 7 items
    • +4Reference files are cited in the instructions (13 of 13)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.